{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# == Hyperparameter configuration ==\n",
        "\n",
        "# Official scored labels Physionet 2021: https://github.com/physionetchallenges/evaluation-2021/blob/main/dx_mapping_scored.csv\n",
        "\n",
        "# 0 = 426783006 -> sinus rhythm (SR)\n",
        "# 1 = 164889003 -> atrial fibrillation (AF)\n",
        "# 2 = 164890007 -> atrial flutter (AFL)\n",
        "# 3 = 284470004 or 63593006 -> premature atrial contraction (PAC) or supraventricular premature beats (SVPB)\n",
        "# 4 = 427172004 or 17338001 -> premature ventricular contractions (PVC), ventricular premature beats (VPB)\n",
        "# 5 = 6374002 -> bundle branch block (BBB)\n",
        "# 6 = 426627000 -> bradycardia (Brady)\n",
        "# 7 = 733534002 or 164909002 -> complete left bundle branch block (CLBBB), left bundle branch block (LBBB)\n",
        "# 8 = 713427006 or 59118001 -> complete right bundle branch block (CRBBB), right bundle branch block (RBBB)\n",
        "# 9 = 270492004 -> 1st degree av block (IAVB)\n",
        "# 10 = 713426002 -> incomplete right bundle branch block (IRBBB)\n",
        "# 11 = 39732003 -> left axis deviation (LAD)\n",
        "# 12 = 445118002 -> left anterior fascicular block (LAnFB)\n",
        "# 13 = 251146004 -> low qrs voltages (LQRSV)\n",
        "# 14 = 698252002 -> nonspecific intraventricular conduction disorder (NSIVCB)\n",
        "# 15 = 10370003 -> pacing rhythm (PR)\n",
        "# 16 = 365413008 -> poor R wave Progression (PRWP)\n",
        "# 17 = 164947007 -> prolonged pr interval (LPR)\n",
        "# 18 = 111975006 -> prolonged qt interval (LQT)\n",
        "# 19 = 164917005 -> qwave abnormal (QAb)\n",
        "# 20 = 47665007 -> right axis deviation (RAD)\n",
        "# 21 = 427393009 -> sinus arrhythmia (SA)\n",
        "# 22 = 426177001 -> sinus bradycardia (SB)\n",
        "# 23 = 427084000 -> sinus tachycardia (STach)\n",
        "# 24 = 164934002 -> t wave abnormal (TAb)\n",
        "# 25 = 59931005 -> t wave inversion (TInv)\n",
        "\n",
        "VALID_LABELS = set(\n",
        "    [\n",
        "        \"164889003\",\n",
        "        \"164890007\",\n",
        "        \"6374002\",\n",
        "        \"426627000\",\n",
        "        \"733534002\",\n",
        "        \"713427006\",\n",
        "        \"270492004\",\n",
        "        \"713426002\",\n",
        "        \"39732003\",\n",
        "        \"445118002\",\n",
        "        \"164909002\",\n",
        "        \"251146004\",\n",
        "        \"698252002\",\n",
        "        \"426783006\",\n",
        "        \"284470004\",\n",
        "        \"10370003\",\n",
        "        \"365413008\",\n",
        "        \"427172004\",\n",
        "        \"164947007\",\n",
        "        \"111975006\",\n",
        "        \"164917005\",\n",
        "        \"47665007\",\n",
        "        \"59118001\",\n",
        "        \"427393009\",\n",
        "        \"426177001\",\n",
        "        \"427084000\",\n",
        "        \"63593006\",\n",
        "        \"164934002\",\n",
        "        \"59931005\",\n",
        "        \"17338001\",\n",
        "    ]\n",
        ")\n",
        "# VALID_LABELS = set([\"426783006\", \"164889003\", \"164890007\", \"284470004\", \"427172004\"]) # SR, AF, AFL, PAC, PVC\n",
        "NUM_CLASSES =  26\n",
        "\n",
        "CLASS_BALANCE = 2000 # imblearn undersampling & SMOTE oversampling\n",
        "TEST_BALANCE = 100\n",
        "TRAIN_TEST_SPLIT = 0.8\n",
        "VALIDATION_SPLIT = 0.25\n",
        "\n",
        "EPOCHS = 500\n",
        "LEARNING_RATE = 0.001\n",
        "BATCH_SIZE = 100"
      ],
      "metadata": {
        "id": "UPiaiebnCYov"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# == Check if GPU is available ==\n",
        "\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68ZDikRKCZ6_",
        "outputId": "3a75b81b-bae9-4a44-ac29-ebdff761d8ed"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Jun 12 13:10:30 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEnUK8J5zqBe",
        "outputId": "62a0a95d-a16e-4554-88e3-8445857def8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (1.11.4)\n",
            "Collecting imblearn\n",
            "  Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (3.5.0)\n",
            "Installing collected packages: imblearn\n",
            "Successfully installed imblearn-0.0\n"
          ]
        }
      ],
      "source": [
        "# == Install requirements ==\n",
        "\n",
        "!pip install google-colab\n",
        "!pip install tensorflow keras numpy\n",
        "!pip install h5py tqdm\n",
        "!pip install pandas scipy imblearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "UD4mNESiD965"
      },
      "outputs": [],
      "source": [
        "# == Import requirements ==\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from google.colab import drive\n",
        "import os\n",
        "import h5py\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "from collections import Counter\n",
        "\n",
        "import random\n",
        "import scipy\n",
        "from scipy.signal import butter, lfilter\n",
        "from sklearn.model_selection import KFold\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.pipeline import Pipeline\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess functions\n",
        "\n",
        "def pad_or_truncate_ecg(ecg: list, max_samples: int) -> list:\n",
        "    try:\n",
        "        padded_or_truncated_ecg = ecg[:max_samples] + [0] * (max_samples - len(ecg))\n",
        "    except Exception as e:\n",
        "        print(\"Fail: padding\", e)\n",
        "    return padded_or_truncated_ecg\n",
        "\n",
        "def resample_ecg(ecg: list, resample: int):\n",
        "    new_ecg = scipy.signal.resample(\n",
        "        ecg, resample, t=None, axis=0, window=None, domain=\"time\"\n",
        "    )\n",
        "    return list(new_ecg)\n",
        "\n",
        "def normalize_to_minus11(ecg: list):\n",
        "    max_val = max(ecg)\n",
        "    min_val = min(ecg)\n",
        "    # Handle the case where max_val and min_val are the same (to avoid division by zero)\n",
        "    if max_val == min_val:\n",
        "        return [0 for _ in ecg]\n",
        "    normalized_values = [2 * (x - min_val) / (max_val - min_val) - 1 for x in ecg]\n",
        "    return normalized_values\n",
        "\n",
        "def butter_bandpass(lowcut, highcut, fs, order=4):\n",
        "    nyquist = 0.5 * fs\n",
        "    low = lowcut / nyquist\n",
        "    high = highcut / nyquist\n",
        "    b, a = butter(order, [low, high], btype=\"band\")\n",
        "    return b, a\n",
        "\n",
        "def butter_bandpass_filter(ecg: list, lowcut: float, highcut: float, sampling_rate: int, order: int =4):\n",
        "    b, a = butter_bandpass(lowcut, highcut, sampling_rate, order=order)\n",
        "    y = lfilter(b, a, ecg)\n",
        "    return y\n",
        "\n",
        "def split_list_into_n_sublists(lst, n):\n",
        "    k, m = divmod(len(lst), n)\n",
        "    return [lst[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n)]"
      ],
      "metadata": {
        "id": "rcryqS0FDqBV"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZTY2UE1Mhlj",
        "outputId": "527cc630-20ac-482a-a1ae-70e8241c4586"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "codes_SNOMED.csv  physionet2017_references.csv\tphysionet2021_references.csv\n",
            "physionet2017.h5  physionet2021.h5\t\tprepared\n"
          ]
        }
      ],
      "source": [
        "# == Mount drive ==\n",
        "\n",
        "# https://drive.google.com/drive/folders/1L_gOMrkygu2N0k97COYuVrmE-AwEEMoQ\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "path = \"/content/drive/My Drive/Master Thesis/Datasets\"\n",
        "!ls \"/content/drive/My Drive/Master Thesis/Datasets\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejP3dsia8Wdq",
        "outputId": "d84f07dc-0f99-42f6-8bf0-e1b2c0372801"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Load ECG data: 100%|██████████| 81960/81960 [02:22<00:00, 575.72it/s]\n",
            "Load ECG labels:  98%|█████████▊| 86738/88252 [00:08<00:00, 20097.15it/s]"
          ]
        }
      ],
      "source": [
        "# == Load all Physionet2021 ECGs and their IDs to a dictionary X_dict ==\n",
        "\n",
        "X_dict = {}\n",
        "Y_dict = {}\n",
        "\n",
        "h5file = h5py.File(os.path.join(path, \"prepared/physionet2021_scoredLabels.h5\"), \"r\")\n",
        "IDs = list(h5file.keys())\n",
        "pbar = tqdm(total=len(IDs), desc=\"Load ECG data\", position=0, leave=True)\n",
        "for key in IDs:\n",
        "    X_dict[key] = list(h5file[key][0])\n",
        "    pbar.update(1)\n",
        "\n",
        "# == Load all labels and their IDs to a dictionary Y_dict (some ECGs can have multiple labels) ==\n",
        "\n",
        "labels_df = pd.read_csv(os.path.join(path, \"physionet2021_references.csv\"), sep=\";\")\n",
        "pbar = tqdm(total=len(labels_df), desc=\"Load ECG labels\", position=0, leave=True)\n",
        "for _, row in labels_df.iterrows():\n",
        "    Y_dict[row[\"id\"]] = row[\"labels\"].split(\",\")\n",
        "    pbar.update(1)\n",
        "\n",
        "del IDs, h5file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "UkTDLTJy9ku0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f3bfb1c-c312-4279-d930-7f17596f2e2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Map labels to ECGs: 100%|██████████| 88252/88252 [00:00<00:00, 568082.46it/s]\n"
          ]
        }
      ],
      "source": [
        "# == Map scored labels to ECGs and create three lists (X: ECGs, Y: labels, Z: IDs) ==\n",
        "\n",
        "X = []\n",
        "Y = []\n",
        "Z = []\n",
        "\n",
        "for patient_id in tqdm(Y_dict, desc=\"Map labels to ECGs\", position=0, leave=True):\n",
        "    for label in Y_dict[patient_id]:\n",
        "        try:\n",
        "            if label in VALID_LABELS:\n",
        "              X.append(X_dict[patient_id])\n",
        "              Y.append(str(label))\n",
        "              Z.append(str(patient_id))\n",
        "        except:\n",
        "            pass\n",
        "\n",
        "del X_dict, Y_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "y2eES-Y84ksK"
      },
      "outputs": [],
      "source": [
        "# Map labels\n",
        "\n",
        "# Official scored labels Physionet 2021: https://github.com/physionetchallenges/evaluation-2021/blob/main/dx_mapping_scored.csv\n",
        "\n",
        "Y = [0 if x == \"426783006\" else x for x in Y] # sinus rhythm (SR)\n",
        "Y = [1 if x == \"164889003\" else x for x in Y] # atrial fibrillation (AF)\n",
        "Y = [2 if x == \"164890007\" else x for x in Y] # atrial flutter (AFL)\n",
        "Y = [3 if x == \"284470004\" or x == \"63593006\" else x for x in Y] # premature atrial contraction (PAC), supraventricular premature beats (SVPB)\n",
        "Y = [4 if x == \"427172004\" or x == \"17338001\" else x for x in Y] # premature ventricular contractions (PVC), ventricular premature beats (VPB)\n",
        "Y = [5 if x == \"6374002\" else x for x in Y] # bundle branch block (BBB)\n",
        "Y = [6 if x == \"426627000\" else x for x in Y] # bradycardia (Brady)\n",
        "Y = [7 if x == \"733534002\" or x == \"164909002\" else x for x in Y] # complete left bundle branch block (CLBBB), left bundle branch block (LBBB)\n",
        "Y = [8 if x == \"713427006\" or x == \"59118001\" else x for x in Y] # complete right bundle branch block (CRBBB), right bundle branch block (RBBB)\n",
        "Y = [9 if x == \"270492004\" else x for x in Y] # 1st degree av block (IAVB)\n",
        "Y = [10 if x == \"713426002\" else x for x in Y] # incomplete right bundle branch block (IRBBB)\n",
        "Y = [11 if x == \"39732003\" else x for x in Y] # left axis deviation (LAD)\n",
        "Y = [12 if x == \"445118002\" else x for x in Y] # left anterior fascicular block (LAnFB)\n",
        "Y = [13 if x == \"251146004\" else x for x in Y] # low qrs voltages (LQRSV)\n",
        "Y = [14 if x == \"698252002\" else x for x in Y] # nonspecific intraventricular conduction disorder (NSIVCB)\n",
        "Y = [15 if x == \"10370003\" else x for x in Y] # pacing rhythm (PR)\n",
        "Y = [16 if x == \"365413008\" else x for x in Y] # poor R wave Progression (PRWP)\n",
        "Y = [17 if x == \"164947007\" else x for x in Y] # prolonged pr interval (LPR)\n",
        "Y = [18 if x == \"111975006\" else x for x in Y] # prolonged qt interval (LQT)\n",
        "Y = [19 if x == \"164917005\" else x for x in Y] # qwave abnormal (QAb)\n",
        "Y = [20 if x == \"47665007\" else x for x in Y] # right axis deviation (RAD)\n",
        "Y = [21 if x == \"427393009\" else x for x in Y] # sinus arrhythmia (SA)\n",
        "Y = [22 if x == \"426177001\" else x for x in Y] # sinus bradycardia (SB)\n",
        "Y = [23 if x == \"427084000\" else x for x in Y] # sinus tachycardia (STach)\n",
        "Y = [24 if x == \"164934002\" else x for x in Y] # t wave abnormal (TAb)\n",
        "Y = [25 if x == \"59931005\" else x for x in Y] # t wave inversion (TInv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8keoyR_gDMaX",
        "outputId": "0885838e-d57e-42b3-bed8-3dcdd75f0101"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Load ECG labels: 100%|██████████| 88252/88252 [00:09<00:00, 9782.53it/s] \n",
            "Preprocess ECGs: 100%|█████████▉| 129351/129357 [04:27<00:00, 491.51it/s]"
          ]
        }
      ],
      "source": [
        "# == Preprocess ECGs ==\n",
        "\n",
        "pbar = tqdm(total=len(X), desc=\"Preprocess ECGs\", position=0, leave=True)\n",
        "for index, _ in enumerate(X):\n",
        "    X[index] = pad_or_truncate_ecg(ecg=X[index], max_samples=5000)\n",
        "    X[index] = resample_ecg(ecg=X[index], resample=2000)\n",
        "    X[index] = normalize_to_minus11(ecg=X[index])\n",
        "    X[index] = butter_bandpass_filter(ecg=X[index], lowcut=0.3, highcut=21.0, sampling_rate=200)\n",
        "    pbar.update(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "S1t3m5cUio4c"
      },
      "outputs": [],
      "source": [
        "# == Split data train-test sets by patients ==\n",
        "\n",
        "bins = []\n",
        "for x in range(NUM_CLASSES):\n",
        "    bins.append([])\n",
        "\n",
        "# Create num_classes bins (1 bin = 1 class)\n",
        "for sample in zip(X, Y, Z):\n",
        "    bins[sample[1]].append([sample[0], sample[1], sample[2]])\n",
        "\n",
        "# Create train-test bins (e.g. TRAIN_TEST_SPLIT = 80%-20%)\n",
        "train_bins = []\n",
        "test_bins = []\n",
        "for x in range(NUM_CLASSES):\n",
        "    train_bins.append([])\n",
        "    test_bins.append([])\n",
        "for index, bin in enumerate(bins):\n",
        "    split_index = int(len(bin) * TRAIN_TEST_SPLIT)\n",
        "    train_bins[index] = bin[:split_index]\n",
        "    test_bins[index] = bin[split_index:]\n",
        "\n",
        "# Create train set (sort bins by occurence of labels descending before adding to train set because of multiple labels and add if patient id is not in train set already)\n",
        "X_train = []\n",
        "Y_train = []\n",
        "Z_train = []\n",
        "id_already_in_x_train = set()\n",
        "train_bins.sort(key=lambda x: len(x))\n",
        "for bin in train_bins:\n",
        "    for sample in bin:\n",
        "        if sample[2] not in id_already_in_x_train:\n",
        "            id_already_in_x_train.add(sample[2])\n",
        "            X_train.append(sample[0])\n",
        "            Y_train.append(sample[1])\n",
        "            Z_train.append(sample[2])\n",
        "\n",
        "# Create test set (sort bins by occurence of labels descending before adding to train set because of multiple labels and add if patient id is not in train (train-test split by patients) or test set already)\n",
        "X_test = []\n",
        "Y_test = []\n",
        "Z_test = []\n",
        "id_already_in_x_test = set()\n",
        "test_bins.sort(key=lambda x: len(x))\n",
        "for bin in test_bins:\n",
        "    for sample in bin:\n",
        "        if sample[2] not in id_already_in_x_train and sample[2] not in id_already_in_x_test:\n",
        "            id_already_in_x_test.add(sample[2])\n",
        "            X_test.append(sample[0])\n",
        "            Y_test.append(sample[1])\n",
        "            Z_test.append(sample[2])\n",
        "\n",
        "# Write train and test patient ids to file\n",
        "id_already_in_x_train_list = list(id_already_in_x_train)\n",
        "id_already_in_x_train_list = list(map(lambda x: str(x) + \"\\n\", id_already_in_x_train_list))\n",
        "id_already_in_x_test_list = list(id_already_in_x_test)\n",
        "id_already_in_x_test_list = list(map(lambda x: str(x) + \"\\n\", id_already_in_x_test_list))\n",
        "with open('trainset_patient_ids.txt', 'w', encoding='utf-8') as file:\n",
        "    file.writelines(id_already_in_x_train_list)\n",
        "with open('testset_patient_ids.txt', 'w', encoding='utf-8') as file:\n",
        "    file.writelines(id_already_in_x_test_list)\n",
        "\n",
        "del X, Y, Z"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Balance data ==\n",
        "\n",
        "# Trainset\n",
        "\n",
        "count_train = Counter(Y_train)\n",
        "\n",
        "undersampling_strategy = {}\n",
        "oversampling_strategy = {}\n",
        "for i in count_train:\n",
        "    if count_train[i] > CLASS_BALANCE:\n",
        "        undersampling_strategy[i] = CLASS_BALANCE\n",
        "    elif count_train[i] <= CLASS_BALANCE:\n",
        "        oversampling_strategy[i] = CLASS_BALANCE\n",
        "print(f\"Trainset undersampling_strategy: {undersampling_strategy}\")\n",
        "print(f\"Trainset oversampling_strategy: {oversampling_strategy}\")\n",
        "\n",
        "under = RandomUnderSampler(sampling_strategy=undersampling_strategy)\n",
        "over = RandomOverSampler(sampling_strategy=oversampling_strategy)\n",
        "steps = [(\"u\", under), (\"o\", over)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "X_train_balanced, Y_train_balanced = pipeline.fit_resample(X_train, Y_train)\n",
        "\n",
        "count_train_balanced = Counter(Y_train_balanced)\n",
        "\n",
        "# Testset\n",
        "\n",
        "count_test = Counter(Y_test)\n",
        "\n",
        "# min_key = min(count_test, key=count_test.get)\n",
        "# min_value = count_test[min_key]\n",
        "\n",
        "undersampling_strategy = {}\n",
        "for i in count_test:\n",
        "    if count_test[i] > TEST_BALANCE:\n",
        "        undersampling_strategy[i] = TEST_BALANCE\n",
        "\n",
        "print(f\"Testset undersampling_strategy: {undersampling_strategy}\")\n",
        "\n",
        "under = RandomUnderSampler(sampling_strategy=undersampling_strategy)\n",
        "steps = [(\"u\", under)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "X_test_balanced, Y_test_balanced = pipeline.fit_resample(X_test, Y_test)\n",
        "\n",
        "count_test_balanced = Counter(Y_test_balanced)\n",
        "\n",
        "print(f\"Trainset: {count_train}\")\n",
        "print(f\"Testset: {count_test}\")\n",
        "print(f\"Trainset balanced: {count_train_balanced}\")\n",
        "print(f\"Testset balanced: {count_test_balanced}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejt6gOg7dPMd",
        "outputId": "bc395c86-6c4b-439a-cdc5-792b9f71e1ba"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainset undersampling_strategy: {3: 2000, 9: 2000, 21: 2000, 8: 2000, 1: 2000, 11: 2000, 2: 2000, 23: 2000, 24: 2000, 22: 2000, 0: 2000}\n",
            "Trainset oversampling_strategy: {6: 2000, 17: 2000, 5: 2000, 16: 2000, 20: 2000, 15: 2000, 7: 2000, 13: 2000, 14: 2000, 10: 2000, 18: 2000, 4: 2000, 19: 2000, 12: 2000, 25: 2000}\n",
            "Testset undersampling_strategy: {20: 100, 15: 100, 7: 100, 13: 100, 14: 100, 10: 100, 18: 100, 4: 100, 19: 100, 12: 100, 3: 100, 9: 100, 21: 100, 25: 100, 8: 100, 1: 100, 11: 100, 2: 100, 23: 100, 24: 100, 22: 100, 0: 100}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rPreprocess ECGs: 100%|██████████| 129357/129357 [04:39<00:00, 491.51it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainset: Counter({0: 17892, 22: 9735, 23: 5162, 11: 3312, 2: 3235, 1: 3185, 8: 2969, 24: 2867, 21: 2567, 9: 2124, 3: 2007, 25: 1756, 12: 1373, 10: 1364, 18: 1322, 14: 1292, 19: 1169, 15: 1155, 13: 1144, 4: 1141, 7: 1123, 20: 982, 16: 470, 5: 414, 17: 313, 6: 236})\n",
            "Testset: Counter({0: 2876, 22: 2404, 23: 915, 11: 779, 2: 600, 1: 589, 24: 400, 12: 332, 10: 295, 15: 283, 21: 273, 8: 255, 14: 239, 7: 185, 3: 182, 25: 153, 9: 149, 20: 143, 18: 137, 19: 120, 4: 116, 13: 111, 17: 65, 6: 44, 16: 6})\n",
            "Trainset balanced: Counter({0: 2000, 1: 2000, 2: 2000, 3: 2000, 4: 2000, 5: 2000, 6: 2000, 7: 2000, 8: 2000, 9: 2000, 10: 2000, 11: 2000, 12: 2000, 13: 2000, 14: 2000, 15: 2000, 16: 2000, 17: 2000, 18: 2000, 19: 2000, 20: 2000, 21: 2000, 22: 2000, 23: 2000, 24: 2000, 25: 2000})\n",
            "Testset balanced: Counter({0: 100, 1: 100, 2: 100, 3: 100, 4: 100, 7: 100, 8: 100, 9: 100, 10: 100, 11: 100, 12: 100, 13: 100, 14: 100, 15: 100, 18: 100, 19: 100, 20: 100, 21: 100, 22: 100, 23: 100, 24: 100, 25: 100, 17: 65, 6: 44, 16: 6})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Shuffle data, convert to numpy lists and reshape ==\n",
        "\n",
        "# Shuffle data\n",
        "\n",
        "combined = list(zip(X_train, Y_train))\n",
        "random.shuffle(combined)\n",
        "X_train, Y_train = zip(*combined)\n",
        "X_train = list(X_train)\n",
        "Y_train = list(Y_train)\n",
        "\n",
        "combined = list(zip(X_test, Y_test))\n",
        "random.shuffle(combined)\n",
        "X_test, Y_test = zip(*combined)\n",
        "X_test = list(X_test)\n",
        "Y_test = list(Y_test)\n",
        "\n",
        "combined = list(zip(X_train_balanced, Y_train_balanced))\n",
        "random.shuffle(combined)\n",
        "X_train_balanced, Y_train_balanced = zip(*combined)\n",
        "X_train_balanced = list(X_train_balanced)\n",
        "Y_train_balanced = list(Y_train_balanced)\n",
        "\n",
        "combined = list(zip(X_test_balanced, Y_test_balanced))\n",
        "random.shuffle(combined)\n",
        "X_test_balanced, Y_test_balanced = zip(*combined)\n",
        "X_test_balanced = list(X_test_balanced)\n",
        "Y_test_balanced = list(Y_test_balanced)\n",
        "\n",
        "print(f\"Y_train: {Y_train[:50]}\")\n",
        "print(f\"Y_test: {Y_test[:50]}\")\n",
        "print(f\"Y_train_balanced: {Y_train_balanced[:50]}\")\n",
        "print(f\"Y_test_balanced: {Y_test_balanced[:50]}\")\n",
        "\n",
        "# Convert to numpy lists\n",
        "\n",
        "Y_train = np.array(Y_train)\n",
        "Y_test = np.array(Y_test)\n",
        "Y_train_balanced = np.array(Y_train_balanced)\n",
        "Y_test_balanced = np.array(Y_test_balanced)\n",
        "\n",
        "for index, x in enumerate(X_train):\n",
        "    X_train[index] = np.array(x)\n",
        "X_train = np.array(X_train)\n",
        "\n",
        "for index, x in enumerate(X_test):\n",
        "    X_test[index] = np.array(x)\n",
        "X_test = np.array(X_test)\n",
        "\n",
        "for index, x in enumerate(X_train_balanced):\n",
        "    X_train_balanced[index] = np.array(x)\n",
        "X_train_balanced = np.array(X_train_balanced)\n",
        "\n",
        "for index, x in enumerate(X_test_balanced):\n",
        "    X_test_balanced[index] = np.array(x)\n",
        "X_test_balanced = np.array(X_test_balanced)\n",
        "\n",
        "# Reshape\n",
        "\n",
        "X_train = X_train.reshape((-1, 2000, 1))\n",
        "X_test = X_test.reshape((-1, 2000, 1))\n",
        "X_train_balanced = X_train_balanced.reshape((-1, 2000, 1))\n",
        "X_test_balanced = X_test_balanced.reshape((-1, 2000, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oH1on-V7RJ1s",
        "outputId": "95dd7b98-b62f-428b-988d-15700ddbb23d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y_train: [1, 22, 0, 22, 1, 11, 22, 0, 1, 0, 23, 19, 9, 1, 14, 22, 0, 25, 15, 23, 15, 21, 0, 23, 23, 10, 0, 14, 3, 22, 0, 10, 22, 1, 0, 22, 2, 21, 23, 22, 0, 22, 11, 15, 17, 22, 9, 19, 23, 0]\n",
            "Y_test: [15, 13, 0, 3, 4, 12, 0, 0, 4, 22, 0, 11, 4, 0, 22, 11, 2, 0, 2, 11, 0, 0, 22, 22, 22, 0, 2, 1, 13, 7, 10, 4, 10, 22, 0, 15, 22, 2, 22, 0, 10, 22, 22, 22, 1, 0, 23, 10, 0, 24]\n",
            "Y_train_balanced: [2, 16, 17, 8, 3, 6, 21, 21, 0, 6, 6, 22, 11, 4, 22, 3, 5, 8, 11, 11, 18, 3, 22, 11, 23, 3, 2, 11, 6, 7, 11, 15, 19, 6, 24, 5, 18, 7, 20, 23, 0, 15, 12, 17, 24, 6, 23, 5, 19, 2]\n",
            "Y_test_balanced: [10, 4, 23, 10, 20, 10, 0, 18, 11, 8, 3, 18, 22, 13, 23, 23, 4, 20, 3, 24, 0, 17, 18, 23, 15, 12, 2, 17, 8, 0, 1, 7, 9, 18, 7, 13, 25, 25, 0, 1, 23, 14, 8, 18, 1, 9, 17, 9, 25, 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "eEsV9r0EjSTw"
      },
      "outputs": [],
      "source": [
        "# == A-B-testing models ==\n",
        "\n",
        "# Model A: Residual CNN\n",
        "\n",
        "def conv(i, filters=16, kernel_size=9, strides=1):\n",
        "    i = keras.layers.Conv1D(\n",
        "        filters=filters, kernel_size=kernel_size, strides=strides, padding=\"same\"\n",
        "    )(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.LeakyReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    return i\n",
        "def residual_unit(x, filters, layers=3):\n",
        "    inp = x\n",
        "    for i in range(layers):\n",
        "        x = conv(x, filters)\n",
        "    return keras.layers.add([x, inp])\n",
        "def conv_block(x, filters, strides):\n",
        "    x = conv(x, filters)\n",
        "    x = residual_unit(x, filters)\n",
        "    if strides > 1:\n",
        "        x = keras.layers.AveragePooling1D(strides, strides)(x)\n",
        "    return x\n",
        "def build_model_A(input_shape, num_classes):\n",
        "    inp = keras.layers.Input(input_shape)\n",
        "    x = inp\n",
        "    x = conv_block(x, 16, 4)\n",
        "    x = conv_block(x, 16, 4)\n",
        "    x = conv_block(x, 32, 4)\n",
        "    x = conv_block(x, 32, 4)\n",
        "    x = keras.layers.Masking(mask_value=0)(x)\n",
        "    x = keras.layers.GRU(128, recurrent_dropout=0.1)(x)\n",
        "    x = keras.layers.Dense(num_classes, activation=\"softmax\")(x)\n",
        "    model = keras.models.Model(inp, x)\n",
        "    return model\n",
        "\n",
        "# Model B: CNN_Transformer_1lead\n",
        "\n",
        "def build_model_B(num_classes, input_shape):\n",
        "    # input_shape = (2000, 1)  # Each sample has 2000 timesteps and 1 feature per timestep\n",
        "    input_layer = keras.layers.Input(input_shape)\n",
        "\n",
        "    # Masking for padded/truncated data\n",
        "    i = keras.layers.Masking(mask_value=0)(input_layer)\n",
        "    # Conv1\n",
        "    i = keras.layers.Conv1D(filters=16, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Conv2\n",
        "    i = keras.layers.Conv1D(filters=32, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Conv3\n",
        "    i = keras.layers.Conv1D(filters=64, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Channel Average Pooling and Reshaping\n",
        "    i = keras.layers.GlobalAveragePooling1D(data_format=\"channels_first\")(i)\n",
        "    i = keras.layers.Reshape((5, 50))(i)\n",
        "    # Encoder block/Attention mechanisms\n",
        "    i = keras.layers.MultiHeadAttention(num_heads=10, key_dim=5, dropout=0.3)(i, i)\n",
        "    # i = keras.layers.MultiHeadAttention(num_heads=8, key_dim=50, dropout=0.3)(i, i)\n",
        "    # i = keras.layers.MultiHeadAttention(num_heads=8, key_dim=50, dropout=0.3)(i, i)\n",
        "    # Flatten\n",
        "    i = keras.layers.Flatten()(i)\n",
        "    # Feedforward Softmax\n",
        "    i = keras.layers.Dense(num_classes, activation=\"softmax\")(i)\n",
        "    return keras.models.Model(inputs=input_layer, outputs=i)\n",
        "\n",
        "# Model C: vanilla_Transformer_1lead\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "def transformer_encoder(input, input_shape, num_heads, key_dim, ff_dim, dropout):\n",
        "    # Multi-Head Attention\n",
        "    x = keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=key_dim, dropout=dropout, kernel_regularizer=regularizers.l2(0.001))(input, input)\n",
        "    # Add & Normalize\n",
        "    res = x + input\n",
        "    x = keras.layers.LayerNormalization(epsilon=1e-6)(res)\n",
        "    # Feed-Forward Layer\n",
        "    x = keras.layers.Flatten(input_shape=input_shape)(x)\n",
        "    x = keras.layers.Dense(units=ff_dim, activation='relu', kernel_regularizer=regularizers.l2(0.001))(x)\n",
        "    x = keras.layers.Dense(input_shape[0] * input_shape[1], kernel_regularizer=regularizers.l2(0.001))(x)\n",
        "    x = keras.layers.Reshape(input_shape)(x)\n",
        "    x = keras.layers.Dropout(rate=dropout)(x)\n",
        "    # Add & Normalize\n",
        "    x = x + res\n",
        "    x = keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
        "    return x\n",
        "\n",
        "def build_transformer_encoder_model(num_classes, input_shape, num_encoder_blocks, num_heads, key_dim, ff_dim, dropout):\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "    x = inputs\n",
        "    for _ in range(num_encoder_blocks):\n",
        "        x = transformer_encoder(x, input_shape, key_dim, num_heads, ff_dim, dropout)\n",
        "    x = keras.layers.Flatten(input_shape=input_shape)(x)\n",
        "    outputs = keras.layers.Dense(units=num_classes, activation='softmax')(x)\n",
        "    return keras.Model(inputs, outputs)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Initialize model ==\n",
        "\n",
        "# Explicitly specify the GPU device\n",
        "physical_devices = tf.config.experimental.list_physical_devices(\"GPU\")\n",
        "if len(physical_devices) > 0:\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
        "\n",
        "input_shape = X_train_balanced.shape\n",
        "num_encoder_blocks = 8\n",
        "num_heads = 8 # 8\n",
        "key_dim = 25 # 25\n",
        "ff_dim = 4 # 24\n",
        "dropout = 0.5\n",
        "\n",
        "# Reshape\n",
        "X_train = X_train.reshape((-1, 2000, 1)) # (-1, 10, 200)\n",
        "X_test = X_test.reshape((-1, 2000, 1))\n",
        "X_train_balanced = X_train_balanced.reshape((-1, 2000, 1))\n",
        "X_test_balanced = X_test_balanced.reshape((-1, 2000, 1))\n",
        "\n",
        "# model = build_transformer_encoder_model(num_classes=NUM_CLASSES, input_shape=input_shape[1:], num_encoder_blocks=num_encoder_blocks, num_heads=num_heads, key_dim=key_dim, ff_dim=ff_dim, dropout=dropout)\n",
        "model = build_model_A(num_classes=NUM_CLASSES, input_shape=input_shape[1:])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yIfrPc8R27b",
        "outputId": "5bf802fa-87f3-4f24-fc4b-34b88eb2bbd8"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_20\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_24 (InputLayer)       [(None, 2000, 1)]            0         []                            \n",
            "                                                                                                  \n",
            " conv1d_12 (Conv1D)          (None, 2000, 16)             160       ['input_24[0][0]']            \n",
            "                                                                                                  \n",
            " batch_normalization_12 (Ba  (None, 2000, 16)             64        ['conv1d_12[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu (LeakyReLU)     (None, 2000, 16)             0         ['batch_normalization_12[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_12 (Spat  (None, 2000, 16)             0         ['leaky_re_lu[0][0]']         \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_13 (Conv1D)          (None, 2000, 16)             2320      ['spatial_dropout1d_12[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_13 (Ba  (None, 2000, 16)             64        ['conv1d_13[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 2000, 16)             0         ['batch_normalization_13[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_13 (Spat  (None, 2000, 16)             0         ['leaky_re_lu_1[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_14 (Conv1D)          (None, 2000, 16)             2320      ['spatial_dropout1d_13[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_14 (Ba  (None, 2000, 16)             64        ['conv1d_14[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 2000, 16)             0         ['batch_normalization_14[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_14 (Spat  (None, 2000, 16)             0         ['leaky_re_lu_2[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_15 (Conv1D)          (None, 2000, 16)             2320      ['spatial_dropout1d_14[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_15 (Ba  (None, 2000, 16)             64        ['conv1d_15[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 2000, 16)             0         ['batch_normalization_15[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_15 (Spat  (None, 2000, 16)             0         ['leaky_re_lu_3[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " add (Add)                   (None, 2000, 16)             0         ['spatial_dropout1d_15[0][0]',\n",
            "                                                                     'spatial_dropout1d_12[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling1d_12 (Aver  (None, 500, 16)              0         ['add[0][0]']                 \n",
            " agePooling1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_16 (Conv1D)          (None, 500, 16)              2320      ['average_pooling1d_12[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 500, 16)              64        ['conv1d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 500, 16)              0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_16 (Spat  (None, 500, 16)              0         ['leaky_re_lu_4[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_17 (Conv1D)          (None, 500, 16)              2320      ['spatial_dropout1d_16[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 500, 16)              64        ['conv1d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_5 (LeakyReLU)   (None, 500, 16)              0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_17 (Spat  (None, 500, 16)              0         ['leaky_re_lu_5[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_18 (Conv1D)          (None, 500, 16)              2320      ['spatial_dropout1d_17[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 500, 16)              64        ['conv1d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_6 (LeakyReLU)   (None, 500, 16)              0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_18 (Spat  (None, 500, 16)              0         ['leaky_re_lu_6[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_19 (Conv1D)          (None, 500, 16)              2320      ['spatial_dropout1d_18[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 500, 16)              64        ['conv1d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_7 (LeakyReLU)   (None, 500, 16)              0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_19 (Spat  (None, 500, 16)              0         ['leaky_re_lu_7[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " add_1 (Add)                 (None, 500, 16)              0         ['spatial_dropout1d_19[0][0]',\n",
            "                                                                     'spatial_dropout1d_16[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling1d_13 (Aver  (None, 125, 16)              0         ['add_1[0][0]']               \n",
            " agePooling1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_20 (Conv1D)          (None, 125, 32)              4640      ['average_pooling1d_13[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 125, 32)              128       ['conv1d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_8 (LeakyReLU)   (None, 125, 32)              0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_20 (Spat  (None, 125, 32)              0         ['leaky_re_lu_8[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_21 (Conv1D)          (None, 125, 32)              9248      ['spatial_dropout1d_20[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 125, 32)              128       ['conv1d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_9 (LeakyReLU)   (None, 125, 32)              0         ['batch_normalization_21[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_21 (Spat  (None, 125, 32)              0         ['leaky_re_lu_9[0][0]']       \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_22 (Conv1D)          (None, 125, 32)              9248      ['spatial_dropout1d_21[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 125, 32)              128       ['conv1d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_10 (LeakyReLU)  (None, 125, 32)              0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_22 (Spat  (None, 125, 32)              0         ['leaky_re_lu_10[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_23 (Conv1D)          (None, 125, 32)              9248      ['spatial_dropout1d_22[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 125, 32)              128       ['conv1d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_11 (LeakyReLU)  (None, 125, 32)              0         ['batch_normalization_23[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_23 (Spat  (None, 125, 32)              0         ['leaky_re_lu_11[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " add_2 (Add)                 (None, 125, 32)              0         ['spatial_dropout1d_23[0][0]',\n",
            "                                                                     'spatial_dropout1d_20[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling1d_14 (Aver  (None, 31, 32)               0         ['add_2[0][0]']               \n",
            " agePooling1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_24 (Conv1D)          (None, 31, 32)               9248      ['average_pooling1d_14[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 31, 32)               128       ['conv1d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_12 (LeakyReLU)  (None, 31, 32)               0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_24 (Spat  (None, 31, 32)               0         ['leaky_re_lu_12[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_25 (Conv1D)          (None, 31, 32)               9248      ['spatial_dropout1d_24[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 31, 32)               128       ['conv1d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_13 (LeakyReLU)  (None, 31, 32)               0         ['batch_normalization_25[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_25 (Spat  (None, 31, 32)               0         ['leaky_re_lu_13[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_26 (Conv1D)          (None, 31, 32)               9248      ['spatial_dropout1d_25[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 31, 32)               128       ['conv1d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_14 (LeakyReLU)  (None, 31, 32)               0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_26 (Spat  (None, 31, 32)               0         ['leaky_re_lu_14[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " conv1d_27 (Conv1D)          (None, 31, 32)               9248      ['spatial_dropout1d_26[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 31, 32)               128       ['conv1d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_15 (LeakyReLU)  (None, 31, 32)               0         ['batch_normalization_27[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout1d_27 (Spat  (None, 31, 32)               0         ['leaky_re_lu_15[0][0]']      \n",
            " ialDropout1D)                                                                                    \n",
            "                                                                                                  \n",
            " add_3 (Add)                 (None, 31, 32)               0         ['spatial_dropout1d_27[0][0]',\n",
            "                                                                     'spatial_dropout1d_24[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling1d_15 (Aver  (None, 7, 32)                0         ['add_3[0][0]']               \n",
            " agePooling1D)                                                                                    \n",
            "                                                                                                  \n",
            " masking_4 (Masking)         (None, 7, 32)                0         ['average_pooling1d_15[0][0]']\n",
            "                                                                                                  \n",
            " gru (GRU)                   (None, 128)                  62208     ['masking_4[0][0]']           \n",
            "                                                                                                  \n",
            " dense_164 (Dense)           (None, 26)                   3354      ['gru[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 152874 (597.16 KB)\n",
            "Trainable params: 152106 (594.16 KB)\n",
            "Non-trainable params: 768 (3.00 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "Z3jbMs35jnOL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cebc2c3-a33a-4e8f-9466-e876c2004206"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of GPUs available: 1\n",
            "Number of training data examples: 52000\n",
            "Number of classes: 26\n",
            "Shape of training data: (52000, 2000, 1)\n",
            "Epochs: 500\n",
            "Learning rate: 0.001\n",
            "Batch size: 100\n",
            "Epoch 1/500\n",
            "390/390 [==============================] - 32s 46ms/step - loss: 2.8321 - sparse_categorical_accuracy: 0.1853 - val_loss: 2.7436 - val_sparse_categorical_accuracy: 0.2059 - lr: 0.0010\n",
            "Epoch 2/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 2.5177 - sparse_categorical_accuracy: 0.2637 - val_loss: 2.3619 - val_sparse_categorical_accuracy: 0.2975 - lr: 0.0010\n",
            "Epoch 3/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 2.4019 - sparse_categorical_accuracy: 0.2899 - val_loss: 2.2774 - val_sparse_categorical_accuracy: 0.3218 - lr: 0.0010\n",
            "Epoch 4/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 2.2795 - sparse_categorical_accuracy: 0.3245 - val_loss: 2.1600 - val_sparse_categorical_accuracy: 0.3529 - lr: 0.0010\n",
            "Epoch 5/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 2.1838 - sparse_categorical_accuracy: 0.3498 - val_loss: 2.1007 - val_sparse_categorical_accuracy: 0.3656 - lr: 0.0010\n",
            "Epoch 6/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 2.1302 - sparse_categorical_accuracy: 0.3648 - val_loss: 2.0667 - val_sparse_categorical_accuracy: 0.3774 - lr: 0.0010\n",
            "Epoch 7/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 2.0864 - sparse_categorical_accuracy: 0.3761 - val_loss: 1.9997 - val_sparse_categorical_accuracy: 0.3914 - lr: 0.0010\n",
            "Epoch 8/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 2.0547 - sparse_categorical_accuracy: 0.3831 - val_loss: 1.9964 - val_sparse_categorical_accuracy: 0.3944 - lr: 0.0010\n",
            "Epoch 9/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 2.0235 - sparse_categorical_accuracy: 0.3914 - val_loss: 1.9699 - val_sparse_categorical_accuracy: 0.4018 - lr: 0.0010\n",
            "Epoch 10/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.9921 - sparse_categorical_accuracy: 0.3997 - val_loss: 1.9477 - val_sparse_categorical_accuracy: 0.4124 - lr: 0.0010\n",
            "Epoch 11/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.9640 - sparse_categorical_accuracy: 0.4075 - val_loss: 1.9548 - val_sparse_categorical_accuracy: 0.4104 - lr: 0.0010\n",
            "Epoch 12/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.9395 - sparse_categorical_accuracy: 0.4136 - val_loss: 1.9203 - val_sparse_categorical_accuracy: 0.4232 - lr: 0.0010\n",
            "Epoch 13/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.9187 - sparse_categorical_accuracy: 0.4188 - val_loss: 1.9077 - val_sparse_categorical_accuracy: 0.4214 - lr: 0.0010\n",
            "Epoch 14/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.9004 - sparse_categorical_accuracy: 0.4238 - val_loss: 1.9134 - val_sparse_categorical_accuracy: 0.4151 - lr: 0.0010\n",
            "Epoch 15/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.8742 - sparse_categorical_accuracy: 0.4298 - val_loss: 1.8293 - val_sparse_categorical_accuracy: 0.4448 - lr: 0.0010\n",
            "Epoch 16/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.8568 - sparse_categorical_accuracy: 0.4363 - val_loss: 1.8094 - val_sparse_categorical_accuracy: 0.4463 - lr: 0.0010\n",
            "Epoch 17/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.8299 - sparse_categorical_accuracy: 0.4411 - val_loss: 1.8205 - val_sparse_categorical_accuracy: 0.4435 - lr: 0.0010\n",
            "Epoch 18/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.8114 - sparse_categorical_accuracy: 0.4472 - val_loss: 1.7908 - val_sparse_categorical_accuracy: 0.4608 - lr: 0.0010\n",
            "Epoch 19/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.7952 - sparse_categorical_accuracy: 0.4526 - val_loss: 1.7605 - val_sparse_categorical_accuracy: 0.4692 - lr: 0.0010\n",
            "Epoch 20/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.7824 - sparse_categorical_accuracy: 0.4565 - val_loss: 1.7738 - val_sparse_categorical_accuracy: 0.4655 - lr: 0.0010\n",
            "Epoch 21/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.7629 - sparse_categorical_accuracy: 0.4609 - val_loss: 1.8397 - val_sparse_categorical_accuracy: 0.4441 - lr: 0.0010\n",
            "Epoch 22/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.7465 - sparse_categorical_accuracy: 0.4645 - val_loss: 1.7237 - val_sparse_categorical_accuracy: 0.4786 - lr: 0.0010\n",
            "Epoch 23/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.7277 - sparse_categorical_accuracy: 0.4728 - val_loss: 1.7230 - val_sparse_categorical_accuracy: 0.4842 - lr: 0.0010\n",
            "Epoch 24/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.7135 - sparse_categorical_accuracy: 0.4772 - val_loss: 1.7600 - val_sparse_categorical_accuracy: 0.4692 - lr: 0.0010\n",
            "Epoch 25/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.6964 - sparse_categorical_accuracy: 0.4816 - val_loss: 1.7034 - val_sparse_categorical_accuracy: 0.4848 - lr: 0.0010\n",
            "Epoch 26/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.6850 - sparse_categorical_accuracy: 0.4816 - val_loss: 1.6773 - val_sparse_categorical_accuracy: 0.5005 - lr: 0.0010\n",
            "Epoch 27/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.6644 - sparse_categorical_accuracy: 0.4904 - val_loss: 1.6783 - val_sparse_categorical_accuracy: 0.4935 - lr: 0.0010\n",
            "Epoch 28/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.6561 - sparse_categorical_accuracy: 0.4926 - val_loss: 1.6792 - val_sparse_categorical_accuracy: 0.4921 - lr: 0.0010\n",
            "Epoch 29/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.6411 - sparse_categorical_accuracy: 0.4975 - val_loss: 1.6467 - val_sparse_categorical_accuracy: 0.5002 - lr: 0.0010\n",
            "Epoch 30/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.6276 - sparse_categorical_accuracy: 0.4993 - val_loss: 1.7188 - val_sparse_categorical_accuracy: 0.4815 - lr: 0.0010\n",
            "Epoch 31/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.6118 - sparse_categorical_accuracy: 0.5000 - val_loss: 1.6298 - val_sparse_categorical_accuracy: 0.5061 - lr: 0.0010\n",
            "Epoch 32/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.6058 - sparse_categorical_accuracy: 0.5069 - val_loss: 1.6414 - val_sparse_categorical_accuracy: 0.5032 - lr: 0.0010\n",
            "Epoch 33/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5890 - sparse_categorical_accuracy: 0.5099 - val_loss: 1.6649 - val_sparse_categorical_accuracy: 0.5037 - lr: 0.0010\n",
            "Epoch 34/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5848 - sparse_categorical_accuracy: 0.5122 - val_loss: 1.6172 - val_sparse_categorical_accuracy: 0.5112 - lr: 0.0010\n",
            "Epoch 35/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5627 - sparse_categorical_accuracy: 0.5184 - val_loss: 1.6248 - val_sparse_categorical_accuracy: 0.5090 - lr: 0.0010\n",
            "Epoch 36/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5613 - sparse_categorical_accuracy: 0.5185 - val_loss: 1.5900 - val_sparse_categorical_accuracy: 0.5184 - lr: 0.0010\n",
            "Epoch 37/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5497 - sparse_categorical_accuracy: 0.5212 - val_loss: 1.6193 - val_sparse_categorical_accuracy: 0.5116 - lr: 0.0010\n",
            "Epoch 38/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5407 - sparse_categorical_accuracy: 0.5247 - val_loss: 1.6202 - val_sparse_categorical_accuracy: 0.5175 - lr: 0.0010\n",
            "Epoch 39/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5283 - sparse_categorical_accuracy: 0.5284 - val_loss: 1.5800 - val_sparse_categorical_accuracy: 0.5255 - lr: 0.0010\n",
            "Epoch 40/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5270 - sparse_categorical_accuracy: 0.5287 - val_loss: 1.5632 - val_sparse_categorical_accuracy: 0.5255 - lr: 0.0010\n",
            "Epoch 41/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5099 - sparse_categorical_accuracy: 0.5318 - val_loss: 1.5635 - val_sparse_categorical_accuracy: 0.5264 - lr: 0.0010\n",
            "Epoch 42/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.5010 - sparse_categorical_accuracy: 0.5329 - val_loss: 1.5656 - val_sparse_categorical_accuracy: 0.5298 - lr: 0.0010\n",
            "Epoch 43/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.4952 - sparse_categorical_accuracy: 0.5369 - val_loss: 1.5775 - val_sparse_categorical_accuracy: 0.5303 - lr: 0.0010\n",
            "Epoch 44/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.4497 - sparse_categorical_accuracy: 0.5488 - val_loss: 1.5221 - val_sparse_categorical_accuracy: 0.5376 - lr: 5.0000e-04\n",
            "Epoch 45/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.4372 - sparse_categorical_accuracy: 0.5533 - val_loss: 1.5257 - val_sparse_categorical_accuracy: 0.5373 - lr: 5.0000e-04\n",
            "Epoch 46/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.4257 - sparse_categorical_accuracy: 0.5557 - val_loss: 1.5307 - val_sparse_categorical_accuracy: 0.5335 - lr: 5.0000e-04\n",
            "Epoch 47/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.4186 - sparse_categorical_accuracy: 0.5565 - val_loss: 1.5093 - val_sparse_categorical_accuracy: 0.5436 - lr: 5.0000e-04\n",
            "Epoch 48/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.4180 - sparse_categorical_accuracy: 0.5564 - val_loss: 1.5202 - val_sparse_categorical_accuracy: 0.5388 - lr: 5.0000e-04\n",
            "Epoch 49/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.4133 - sparse_categorical_accuracy: 0.5601 - val_loss: 1.5155 - val_sparse_categorical_accuracy: 0.5456 - lr: 5.0000e-04\n",
            "Epoch 50/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.4042 - sparse_categorical_accuracy: 0.5603 - val_loss: 1.5027 - val_sparse_categorical_accuracy: 0.5450 - lr: 5.0000e-04\n",
            "Epoch 51/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3941 - sparse_categorical_accuracy: 0.5644 - val_loss: 1.5074 - val_sparse_categorical_accuracy: 0.5451 - lr: 5.0000e-04\n",
            "Epoch 52/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3894 - sparse_categorical_accuracy: 0.5653 - val_loss: 1.5125 - val_sparse_categorical_accuracy: 0.5456 - lr: 5.0000e-04\n",
            "Epoch 53/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3890 - sparse_categorical_accuracy: 0.5656 - val_loss: 1.4923 - val_sparse_categorical_accuracy: 0.5479 - lr: 5.0000e-04\n",
            "Epoch 54/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3867 - sparse_categorical_accuracy: 0.5658 - val_loss: 1.5080 - val_sparse_categorical_accuracy: 0.5430 - lr: 5.0000e-04\n",
            "Epoch 55/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3832 - sparse_categorical_accuracy: 0.5686 - val_loss: 1.4919 - val_sparse_categorical_accuracy: 0.5498 - lr: 5.0000e-04\n",
            "Epoch 56/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3702 - sparse_categorical_accuracy: 0.5733 - val_loss: 1.4981 - val_sparse_categorical_accuracy: 0.5465 - lr: 5.0000e-04\n",
            "Epoch 57/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3688 - sparse_categorical_accuracy: 0.5726 - val_loss: 1.4975 - val_sparse_categorical_accuracy: 0.5457 - lr: 5.0000e-04\n",
            "Epoch 58/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3662 - sparse_categorical_accuracy: 0.5727 - val_loss: 1.4935 - val_sparse_categorical_accuracy: 0.5502 - lr: 5.0000e-04\n",
            "Epoch 59/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3436 - sparse_categorical_accuracy: 0.5815 - val_loss: 1.4713 - val_sparse_categorical_accuracy: 0.5538 - lr: 2.5000e-04\n",
            "Epoch 60/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3343 - sparse_categorical_accuracy: 0.5808 - val_loss: 1.4756 - val_sparse_categorical_accuracy: 0.5535 - lr: 2.5000e-04\n",
            "Epoch 61/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3381 - sparse_categorical_accuracy: 0.5812 - val_loss: 1.4784 - val_sparse_categorical_accuracy: 0.5505 - lr: 2.5000e-04\n",
            "Epoch 62/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3243 - sparse_categorical_accuracy: 0.5860 - val_loss: 1.4755 - val_sparse_categorical_accuracy: 0.5552 - lr: 2.5000e-04\n",
            "Epoch 63/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3158 - sparse_categorical_accuracy: 0.5864 - val_loss: 1.4683 - val_sparse_categorical_accuracy: 0.5563 - lr: 1.2500e-04\n",
            "Epoch 64/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3178 - sparse_categorical_accuracy: 0.5869 - val_loss: 1.4635 - val_sparse_categorical_accuracy: 0.5562 - lr: 1.2500e-04\n",
            "Epoch 65/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3141 - sparse_categorical_accuracy: 0.5894 - val_loss: 1.4633 - val_sparse_categorical_accuracy: 0.5592 - lr: 1.2500e-04\n",
            "Epoch 66/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3139 - sparse_categorical_accuracy: 0.5886 - val_loss: 1.4647 - val_sparse_categorical_accuracy: 0.5571 - lr: 1.2500e-04\n",
            "Epoch 67/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.3034 - sparse_categorical_accuracy: 0.5894 - val_loss: 1.4628 - val_sparse_categorical_accuracy: 0.5579 - lr: 1.2500e-04\n",
            "Epoch 68/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3073 - sparse_categorical_accuracy: 0.5893 - val_loss: 1.4618 - val_sparse_categorical_accuracy: 0.5605 - lr: 1.2500e-04\n",
            "Epoch 69/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3020 - sparse_categorical_accuracy: 0.5910 - val_loss: 1.4601 - val_sparse_categorical_accuracy: 0.5605 - lr: 1.2500e-04\n",
            "Epoch 70/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3048 - sparse_categorical_accuracy: 0.5898 - val_loss: 1.4596 - val_sparse_categorical_accuracy: 0.5598 - lr: 1.2500e-04\n",
            "Epoch 71/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2961 - sparse_categorical_accuracy: 0.5937 - val_loss: 1.4601 - val_sparse_categorical_accuracy: 0.5614 - lr: 1.2500e-04\n",
            "Epoch 72/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.2962 - sparse_categorical_accuracy: 0.5951 - val_loss: 1.4568 - val_sparse_categorical_accuracy: 0.5614 - lr: 1.2500e-04\n",
            "Epoch 73/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2948 - sparse_categorical_accuracy: 0.5935 - val_loss: 1.4616 - val_sparse_categorical_accuracy: 0.5602 - lr: 1.2500e-04\n",
            "Epoch 74/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.3038 - sparse_categorical_accuracy: 0.5915 - val_loss: 1.4572 - val_sparse_categorical_accuracy: 0.5611 - lr: 1.2500e-04\n",
            "Epoch 75/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2932 - sparse_categorical_accuracy: 0.5942 - val_loss: 1.4519 - val_sparse_categorical_accuracy: 0.5629 - lr: 1.2500e-04\n",
            "Epoch 76/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2930 - sparse_categorical_accuracy: 0.5959 - val_loss: 1.4562 - val_sparse_categorical_accuracy: 0.5617 - lr: 1.2500e-04\n",
            "Epoch 77/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2991 - sparse_categorical_accuracy: 0.5922 - val_loss: 1.4529 - val_sparse_categorical_accuracy: 0.5596 - lr: 1.2500e-04\n",
            "Epoch 78/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2894 - sparse_categorical_accuracy: 0.5949 - val_loss: 1.4562 - val_sparse_categorical_accuracy: 0.5603 - lr: 1.2500e-04\n",
            "Epoch 79/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2842 - sparse_categorical_accuracy: 0.5967 - val_loss: 1.4544 - val_sparse_categorical_accuracy: 0.5625 - lr: 6.2500e-05\n",
            "Epoch 80/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.2842 - sparse_categorical_accuracy: 0.5944 - val_loss: 1.4518 - val_sparse_categorical_accuracy: 0.5614 - lr: 6.2500e-05\n",
            "Epoch 81/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2835 - sparse_categorical_accuracy: 0.5977 - val_loss: 1.4500 - val_sparse_categorical_accuracy: 0.5618 - lr: 6.2500e-05\n",
            "Epoch 82/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2858 - sparse_categorical_accuracy: 0.5979 - val_loss: 1.4517 - val_sparse_categorical_accuracy: 0.5610 - lr: 6.2500e-05\n",
            "Epoch 83/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.2820 - sparse_categorical_accuracy: 0.5945 - val_loss: 1.4483 - val_sparse_categorical_accuracy: 0.5623 - lr: 6.2500e-05\n",
            "Epoch 84/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2813 - sparse_categorical_accuracy: 0.5965 - val_loss: 1.4496 - val_sparse_categorical_accuracy: 0.5605 - lr: 6.2500e-05\n",
            "Epoch 85/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2804 - sparse_categorical_accuracy: 0.6002 - val_loss: 1.4501 - val_sparse_categorical_accuracy: 0.5628 - lr: 6.2500e-05\n",
            "Epoch 86/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2769 - sparse_categorical_accuracy: 0.5987 - val_loss: 1.4507 - val_sparse_categorical_accuracy: 0.5614 - lr: 6.2500e-05\n",
            "Epoch 87/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2778 - sparse_categorical_accuracy: 0.5996 - val_loss: 1.4492 - val_sparse_categorical_accuracy: 0.5625 - lr: 3.1250e-05\n",
            "Epoch 88/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.2778 - sparse_categorical_accuracy: 0.5992 - val_loss: 1.4470 - val_sparse_categorical_accuracy: 0.5636 - lr: 3.1250e-05\n",
            "Epoch 89/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2746 - sparse_categorical_accuracy: 0.6005 - val_loss: 1.4474 - val_sparse_categorical_accuracy: 0.5640 - lr: 3.1250e-05\n",
            "Epoch 90/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2787 - sparse_categorical_accuracy: 0.5976 - val_loss: 1.4478 - val_sparse_categorical_accuracy: 0.5628 - lr: 3.1250e-05\n",
            "Epoch 91/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.2770 - sparse_categorical_accuracy: 0.5994 - val_loss: 1.4464 - val_sparse_categorical_accuracy: 0.5642 - lr: 3.1250e-05\n",
            "Epoch 92/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2739 - sparse_categorical_accuracy: 0.6004 - val_loss: 1.4464 - val_sparse_categorical_accuracy: 0.5628 - lr: 3.1250e-05\n",
            "Epoch 93/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2717 - sparse_categorical_accuracy: 0.5983 - val_loss: 1.4472 - val_sparse_categorical_accuracy: 0.5632 - lr: 3.1250e-05\n",
            "Epoch 94/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2708 - sparse_categorical_accuracy: 0.6002 - val_loss: 1.4479 - val_sparse_categorical_accuracy: 0.5635 - lr: 3.1250e-05\n",
            "Epoch 95/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2732 - sparse_categorical_accuracy: 0.5992 - val_loss: 1.4473 - val_sparse_categorical_accuracy: 0.5632 - lr: 1.5625e-05\n",
            "Epoch 96/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2666 - sparse_categorical_accuracy: 0.6044 - val_loss: 1.4462 - val_sparse_categorical_accuracy: 0.5636 - lr: 1.5625e-05\n",
            "Epoch 97/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2721 - sparse_categorical_accuracy: 0.6011 - val_loss: 1.4475 - val_sparse_categorical_accuracy: 0.5640 - lr: 1.5625e-05\n",
            "Epoch 98/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2724 - sparse_categorical_accuracy: 0.5988 - val_loss: 1.4473 - val_sparse_categorical_accuracy: 0.5631 - lr: 1.5625e-05\n",
            "Epoch 99/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2680 - sparse_categorical_accuracy: 0.6024 - val_loss: 1.4454 - val_sparse_categorical_accuracy: 0.5634 - lr: 1.5625e-05\n",
            "Epoch 100/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2729 - sparse_categorical_accuracy: 0.5974 - val_loss: 1.4461 - val_sparse_categorical_accuracy: 0.5651 - lr: 1.5625e-05\n",
            "Epoch 101/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2693 - sparse_categorical_accuracy: 0.6014 - val_loss: 1.4461 - val_sparse_categorical_accuracy: 0.5644 - lr: 1.5625e-05\n",
            "Epoch 102/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2763 - sparse_categorical_accuracy: 0.5973 - val_loss: 1.4463 - val_sparse_categorical_accuracy: 0.5636 - lr: 1.5625e-05\n",
            "Epoch 103/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2740 - sparse_categorical_accuracy: 0.5973 - val_loss: 1.4453 - val_sparse_categorical_accuracy: 0.5638 - lr: 7.8125e-06\n",
            "Epoch 104/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.2646 - sparse_categorical_accuracy: 0.6010 - val_loss: 1.4447 - val_sparse_categorical_accuracy: 0.5638 - lr: 7.8125e-06\n",
            "Epoch 105/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2698 - sparse_categorical_accuracy: 0.6007 - val_loss: 1.4454 - val_sparse_categorical_accuracy: 0.5635 - lr: 7.8125e-06\n",
            "Epoch 106/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2707 - sparse_categorical_accuracy: 0.5988 - val_loss: 1.4447 - val_sparse_categorical_accuracy: 0.5632 - lr: 7.8125e-06\n",
            "Epoch 107/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2719 - sparse_categorical_accuracy: 0.6002 - val_loss: 1.4456 - val_sparse_categorical_accuracy: 0.5640 - lr: 7.8125e-06\n",
            "Epoch 108/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2681 - sparse_categorical_accuracy: 0.6008 - val_loss: 1.4452 - val_sparse_categorical_accuracy: 0.5641 - lr: 5.0000e-06\n",
            "Epoch 109/500\n",
            "390/390 [==============================] - 17s 44ms/step - loss: 1.2742 - sparse_categorical_accuracy: 0.5975 - val_loss: 1.4452 - val_sparse_categorical_accuracy: 0.5643 - lr: 5.0000e-06\n",
            "Epoch 110/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2693 - sparse_categorical_accuracy: 0.6006 - val_loss: 1.4461 - val_sparse_categorical_accuracy: 0.5638 - lr: 5.0000e-06\n",
            "Epoch 111/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2711 - sparse_categorical_accuracy: 0.6017 - val_loss: 1.4455 - val_sparse_categorical_accuracy: 0.5643 - lr: 5.0000e-06\n",
            "Epoch 112/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2676 - sparse_categorical_accuracy: 0.6010 - val_loss: 1.4456 - val_sparse_categorical_accuracy: 0.5641 - lr: 5.0000e-06\n",
            "Epoch 113/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2736 - sparse_categorical_accuracy: 0.5988 - val_loss: 1.4456 - val_sparse_categorical_accuracy: 0.5641 - lr: 5.0000e-06\n",
            "Epoch 114/500\n",
            "390/390 [==============================] - 17s 43ms/step - loss: 1.2649 - sparse_categorical_accuracy: 0.6010 - val_loss: 1.4456 - val_sparse_categorical_accuracy: 0.5632 - lr: 5.0000e-06\n",
            "Epoch 114: early stopping\n"
          ]
        }
      ],
      "source": [
        "# == Train model ==\n",
        "\n",
        "# Check if a GPU is available\n",
        "print(\"Number of GPUs available:\", len(tf.config.experimental.list_physical_devices(\"GPU\")))\n",
        "print(f\"Number of training data examples: {len(X_train_balanced)}\")\n",
        "print(f\"Number of classes: {NUM_CLASSES}\")\n",
        "print(f\"Shape of training data: {input_shape}\")\n",
        "print(f\"Epochs: {EPOCHS}\")\n",
        "print(f\"Learning rate: {LEARNING_RATE}\")\n",
        "print(f\"Batch size: {BATCH_SIZE}\")\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"Physionet2021_trainset_Transformer_scored.h5\", save_best_only=True, monitor=\"val_loss\"),\n",
        "    keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=3, min_lr=0.000005),\n",
        "    keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=10, verbose=1),\n",
        "]\n",
        "\n",
        "# optimizer = Adam(learning_rate=LEARNING_RATE)\n",
        "optimizer = Adam(learning_rate=LEARNING_RATE)\n",
        "\n",
        "\n",
        "# loss=\"binary_crossentropy\", metrics=[\"binary_accuracy\"]; sparse_categorical_crossentropy\n",
        "model.compile(optimizer=optimizer, loss=\"sparse_categorical_crossentropy\", metrics=[\"sparse_categorical_accuracy\"])\n",
        "history = model.fit(X_train_balanced, Y_train_balanced, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=callbacks, validation_split=0.25, verbose=1)\n",
        "\n",
        "train_accuracy = history.history[\"sparse_categorical_accuracy\"] # list\n",
        "val_accuracy = history.history[\"val_sparse_categorical_accuracy\"] # list\n",
        "train_loss = history.history[\"loss\"] # list\n",
        "val_loss = history.history[\"val_loss\"] # list"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Plot training curve ==\n",
        "\n",
        "# Accuracy\n",
        "\n",
        "train_accuracy = train_accuracy\n",
        "val_accuracy = val_accuracy\n",
        "epochs_range = range(1, len(train_accuracy) + 1)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(epochs_range, train_accuracy, \"r\", label=\"Training Accuracy\")\n",
        "plt.plot(epochs_range, val_accuracy, \"b\", label=\"Validation Accuracy\")\n",
        "plt.title(\"Training and Validation Accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.savefig(\n",
        "    \"Training_accuracy_\"+str(NUM_CLASSES)+\"classes.png\",\n",
        "    dpi=300,\n",
        "    format=\"png\",\n",
        "    bbox_inches=\"tight\",\n",
        ")\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "# Loss\n",
        "\n",
        "train_loss = train_loss\n",
        "val_loss = val_loss\n",
        "epochs_range = range(1, len(train_loss) + 1)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(epochs_range, train_loss, \"r\", label=\"Training Loss\")\n",
        "plt.plot(epochs_range, val_loss, \"b\", label=\"Validation Loss\")\n",
        "plt.title(\"Training and Validation Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.savefig(\n",
        "    \"Training_loss_\"+str(NUM_CLASSES)+\"classes.png\",\n",
        "    dpi=300,\n",
        "    format=\"png\",\n",
        "    bbox_inches=\"tight\",\n",
        ")\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "id": "Rtmwe0Z-qO2n",
        "outputId": "88fe3c2c-66b6-40e6-aaa2-8470275430e7"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Test model on testset ==\n",
        "\n",
        "pred_prob = model.predict(X_test)\n",
        "pred = np.argmax(pred_prob, axis=-1)\n",
        "print(f\"Accuracy testset: {accuracy_score(Y_test, pred)}\")\n",
        "\n",
        "pred_prob_balanced = model.predict(X_test_balanced)\n",
        "pred_balanced = np.argmax(pred_prob_balanced, axis=-1)\n",
        "print(f\"Accuracy testset balanced: {accuracy_score(Y_test_balanced, pred_balanced)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVMpBgwYqShB",
        "outputId": "f809d22b-0333-49c8-807a-dc552aed207c"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "365/365 [==============================] - 4s 7ms/step\n",
            "Accuracy testset: 0.4415929963093297\n",
            "73/73 [==============================] - 1s 9ms/step\n",
            "Accuracy testset balanced: 0.33779697624190064\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Confusion matrix ==\n",
        "\n",
        "if NUM_CLASSES == 26:\n",
        "    labels = [\n",
        "        \"SR\",  # sinus rhythm\n",
        "        \"AF\",  # atrial fibrillation\n",
        "        \"AFL\",  # atrial flutter\n",
        "        \"PAC/SVPB\",  # premature atrial contraction / supraventricular premature beats\n",
        "        \"PVC/VPB\",  # premature ventricular contractions / ventricular premature beats\n",
        "        \"BBB\",  # bundle branch block\n",
        "        \"Brady\",  # bradycardia\n",
        "        \"CLBBB/LBBB\",  # complete left bundle branch block / left bundle branch block\n",
        "        \"CRBBB/RBBB\",  # complete right bundle branch block / right bundle branch block\n",
        "        \"IAVB\",  # 1st degree AV block\n",
        "        \"IRBBB\",  # incomplete right bundle branch block\n",
        "        \"LAD\",  # left axis deviation\n",
        "        \"LAnFB\",  # left anterior fascicular block\n",
        "        \"LQRSV\",  # low QRS voltages\n",
        "        \"NSIVCB\",  # nonspecific intraventricular conduction disorder\n",
        "        \"PR\",  # pacing rhythm\n",
        "        \"PRWP\",  # poor R wave progression\n",
        "        \"LPR\",  # prolonged PR interval\n",
        "        \"LQT\",  # prolonged QT interval\n",
        "        \"QAb\",  # Q wave abnormal\n",
        "        \"RAD\",  # right axis deviation\n",
        "        \"SA\",  # sinus arrhythmia\n",
        "        \"SB\",  # sinus bradycardia\n",
        "        \"STach\",  # sinus tachycardia\n",
        "        \"TAb\",  # T wave abnormal\n",
        "        \"TInv\"  # T wave inversion\n",
        "    ]\n",
        "else:\n",
        "    labels = [\n",
        "        \"SR\",  # sinus rhythm\n",
        "        \"AF\",  # atrial fibrillation\n",
        "        \"AFL\",  # atrial flutter\n",
        "        \"PAC/SVPB\",  # premature atrial contraction / supraventricular premature beats\n",
        "        \"PVC/VPB\",  # premature ventricular contractions / ventricular premature beats\n",
        "    ]\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "cm = confusion_matrix(Y_test, pred)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "plt.title('Physionet 2021')\n",
        "plt.ylabel('Actual Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.savefig(\"ConfusionMatrix_Physionet2021.png\")\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "cm = confusion_matrix(Y_test_balanced, pred_balanced)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "plt.title('Physionet 2021')\n",
        "plt.ylabel('Actual Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.savefig(\"ConfusionMatrix_Physionet2021.png\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QiCXS9oYq644",
        "outputId": "ba8a1cc6-93ca-4f22-8b55-1746deca1066"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}